An Optimized Solar PV Model Parameter Extraction Technique Using Lungs Performance-Based Optimization

Rahul Khajuria; Ananad Krishan Sharma; Pankaj Sharma; Rajesh Kumar; Ravita Lamba; Saravanakumar Raju

doi:10.1007/978-981-96-5955-5_34

An Optimized Solar PV Model Parameter Extraction Technique Using Lungs Performance-Based Optimization

Rahul Khajuria
, Ananad Krishan Sharma
, Pankaj Sharma
, Rajesh Kumar
, Ravita Lamba
, Saravanakumar Raju

Human Anatomy and Physiology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Accurate determination of photovoltaic (PV) model parameters is essential for studying the variables that affect PV power generation efficiency. The extraction of parameters from the PV model presents a challenging problem due to its multi-model as well as nonlinear characteristics. This paper presents a solution to this challenge through the utilization of the Lungs Performance-Based Optimization (LPO) algorithm. The parameter identification technique has been described as an optimization problem focused on minimizing the current-based root mean squared error (RMSE). Additionally, the LPO algorithm is adopted in triple diode model (TDM) parameter extraction for RTC France, and Photowatt-PWP201 solar PV modules. The outcomes indicate that the LPO algorithm combined with the Newton–Raphson method demonstrates superior robustness and convergence accuracy contrasted to the other meta-heuristics (MH) algorithm with obtained minimum RMSE value of 7.3478E-04 and 2.0528E-03 for RTC France and Photowatt-PWP201 solar PV modules, respectively. Furthermore, an extensive performance evaluation of the MH algorithms has been performed, utilizing convergence curves, boxplots, as well as characteristic curves for current (I) versus voltage (V) and power (P) versus voltage (V), derived from the experimental outcomes.

Original language	English
Title of host publication	Soft Computing
Subtitle of host publication	Theories and Applications - Proceedings of SoCTA 2024
Editors	Rajesh Kumar, Ajit Kumar Verma, Om Prakash Verma, Jitendra Rajpurohit
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	399-410
Number of pages	12
ISBN (Print)	9789819659548
DOIs	https://doi.org/10.1007/978-981-96-5955-5_34
Publication status	Published - 2025
Event	9th International Conference on Soft Computing: Theories and Applications, SoCTA 2024 - Jaipur, India Duration: 27 Dec 2024 → 29 Dec 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1343 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	9th International Conference on Soft Computing: Theories and Applications, SoCTA 2024
Country/Territory	India
City	Jaipur
Period	27/12/24 → 29/12/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

I–V characteristics
Lungs performance-based optimization
Parameter identification
Solar PV
Statistical study

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-96-5955-5_34

Cite this

Khajuria, R., Sharma, A. K., Sharma, P., Kumar, R., Lamba, R., & Raju, S. (2025). An Optimized Solar PV Model Parameter Extraction Technique Using Lungs Performance-Based Optimization. In R. Kumar, A. K. Verma, O. P. Verma, & J. Rajpurohit (Eds.), Soft Computing: Theories and Applications - Proceedings of SoCTA 2024 (pp. 399-410). (Lecture Notes in Networks and Systems; Vol. 1343 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-5955-5_34

Khajuria, Rahul ; Sharma, Ananad Krishan ; Sharma, Pankaj et al. / An Optimized Solar PV Model Parameter Extraction Technique Using Lungs Performance-Based Optimization. Soft Computing: Theories and Applications - Proceedings of SoCTA 2024. editor / Rajesh Kumar ; Ajit Kumar Verma ; Om Prakash Verma ; Jitendra Rajpurohit. Springer Science and Business Media Deutschland GmbH, 2025. pp. 399-410 (Lecture Notes in Networks and Systems).

@inproceedings{3d663530bc6e4beabd6d2af69976bac5,

title = "An Optimized Solar PV Model Parameter Extraction Technique Using Lungs Performance-Based Optimization",

abstract = "Accurate determination of photovoltaic (PV) model parameters is essential for studying the variables that affect PV power generation efficiency. The extraction of parameters from the PV model presents a challenging problem due to its multi-model as well as nonlinear characteristics. This paper presents a solution to this challenge through the utilization of the Lungs Performance-Based Optimization (LPO) algorithm. The parameter identification technique has been described as an optimization problem focused on minimizing the current-based root mean squared error (RMSE). Additionally, the LPO algorithm is adopted in triple diode model (TDM) parameter extraction for RTC France, and Photowatt-PWP201 solar PV modules. The outcomes indicate that the LPO algorithm combined with the Newton–Raphson method demonstrates superior robustness and convergence accuracy contrasted to the other meta-heuristics (MH) algorithm with obtained minimum RMSE value of 7.3478E-04 and 2.0528E-03 for RTC France and Photowatt-PWP201 solar PV modules, respectively. Furthermore, an extensive performance evaluation of the MH algorithms has been performed, utilizing convergence curves, boxplots, as well as characteristic curves for current (I) versus voltage (V) and power (P) versus voltage (V), derived from the experimental outcomes.",

keywords = "I–V characteristics, Lungs performance-based optimization, Parameter identification, Solar PV, Statistical study",

author = "Rahul Khajuria and Sharma, \{Ananad Krishan\} and Pankaj Sharma and Rajesh Kumar and Ravita Lamba and Saravanakumar Raju",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 9th International Conference on Soft Computing: Theories and Applications, SoCTA 2024 ; Conference date: 27-12-2024 Through 29-12-2024",

year = "2025",

doi = "10.1007/978-981-96-5955-5\_34",

language = "English",

isbn = "9789819659548",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "399--410",

editor = "Rajesh Kumar and Verma, \{Ajit Kumar\} and Verma, \{Om Prakash\} and Jitendra Rajpurohit",

booktitle = "Soft Computing",

address = "Germany",

}

Khajuria, R, Sharma, AK, Sharma, P, Kumar, R, Lamba, R & Raju, S 2025, An Optimized Solar PV Model Parameter Extraction Technique Using Lungs Performance-Based Optimization. in R Kumar, AK Verma, OP Verma & J Rajpurohit (eds), Soft Computing: Theories and Applications - Proceedings of SoCTA 2024. Lecture Notes in Networks and Systems, vol. 1343 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 399-410, 9th International Conference on Soft Computing: Theories and Applications, SoCTA 2024, Jaipur, India, 27/12/24. https://doi.org/10.1007/978-981-96-5955-5_34

An Optimized Solar PV Model Parameter Extraction Technique Using Lungs Performance-Based Optimization. / Khajuria, Rahul; Sharma, Ananad Krishan; Sharma, Pankaj et al.
Soft Computing: Theories and Applications - Proceedings of SoCTA 2024. ed. / Rajesh Kumar; Ajit Kumar Verma; Om Prakash Verma; Jitendra Rajpurohit. Springer Science and Business Media Deutschland GmbH, 2025. p. 399-410 (Lecture Notes in Networks and Systems; Vol. 1343 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Optimized Solar PV Model Parameter Extraction Technique Using Lungs Performance-Based Optimization

AU - Khajuria, Rahul

AU - Sharma, Ananad Krishan

AU - Sharma, Pankaj

AU - Kumar, Rajesh

AU - Lamba, Ravita

AU - Raju, Saravanakumar

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

PY - 2025

Y1 - 2025

N2 - Accurate determination of photovoltaic (PV) model parameters is essential for studying the variables that affect PV power generation efficiency. The extraction of parameters from the PV model presents a challenging problem due to its multi-model as well as nonlinear characteristics. This paper presents a solution to this challenge through the utilization of the Lungs Performance-Based Optimization (LPO) algorithm. The parameter identification technique has been described as an optimization problem focused on minimizing the current-based root mean squared error (RMSE). Additionally, the LPO algorithm is adopted in triple diode model (TDM) parameter extraction for RTC France, and Photowatt-PWP201 solar PV modules. The outcomes indicate that the LPO algorithm combined with the Newton–Raphson method demonstrates superior robustness and convergence accuracy contrasted to the other meta-heuristics (MH) algorithm with obtained minimum RMSE value of 7.3478E-04 and 2.0528E-03 for RTC France and Photowatt-PWP201 solar PV modules, respectively. Furthermore, an extensive performance evaluation of the MH algorithms has been performed, utilizing convergence curves, boxplots, as well as characteristic curves for current (I) versus voltage (V) and power (P) versus voltage (V), derived from the experimental outcomes.

AB - Accurate determination of photovoltaic (PV) model parameters is essential for studying the variables that affect PV power generation efficiency. The extraction of parameters from the PV model presents a challenging problem due to its multi-model as well as nonlinear characteristics. This paper presents a solution to this challenge through the utilization of the Lungs Performance-Based Optimization (LPO) algorithm. The parameter identification technique has been described as an optimization problem focused on minimizing the current-based root mean squared error (RMSE). Additionally, the LPO algorithm is adopted in triple diode model (TDM) parameter extraction for RTC France, and Photowatt-PWP201 solar PV modules. The outcomes indicate that the LPO algorithm combined with the Newton–Raphson method demonstrates superior robustness and convergence accuracy contrasted to the other meta-heuristics (MH) algorithm with obtained minimum RMSE value of 7.3478E-04 and 2.0528E-03 for RTC France and Photowatt-PWP201 solar PV modules, respectively. Furthermore, an extensive performance evaluation of the MH algorithms has been performed, utilizing convergence curves, boxplots, as well as characteristic curves for current (I) versus voltage (V) and power (P) versus voltage (V), derived from the experimental outcomes.

KW - I–V characteristics

KW - Lungs performance-based optimization

KW - Parameter identification

KW - Solar PV

KW - Statistical study

UR - https://www.scopus.com/pages/publications/105016215476

U2 - 10.1007/978-981-96-5955-5_34

DO - 10.1007/978-981-96-5955-5_34

M3 - Conference contribution

AN - SCOPUS:105016215476

SN - 9789819659548

T3 - Lecture Notes in Networks and Systems

SP - 399

EP - 410

BT - Soft Computing

A2 - Kumar, Rajesh

A2 - Verma, Ajit Kumar

A2 - Verma, Om Prakash

A2 - Rajpurohit, Jitendra

PB - Springer Science and Business Media Deutschland GmbH

T2 - 9th International Conference on Soft Computing: Theories and Applications, SoCTA 2024

Y2 - 27 December 2024 through 29 December 2024

ER -

Khajuria R, Sharma AK, Sharma P, Kumar R, Lamba R, Raju S. An Optimized Solar PV Model Parameter Extraction Technique Using Lungs Performance-Based Optimization. In Kumar R, Verma AK, Verma OP, Rajpurohit J, editors, Soft Computing: Theories and Applications - Proceedings of SoCTA 2024. Springer Science and Business Media Deutschland GmbH. 2025. p. 399-410. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-96-5955-5_34

An Optimized Solar PV Model Parameter Extraction Technique Using Lungs Performance-Based Optimization

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this